Who gets to understand the systems that govern wealth? And who gets left guessing?

That question drives my work. I’m a PhD student in Finance at UCLA Anderson, studying how machine learning and high-dimensional econometrics can make financial systems more legible — not just to specialists, but to anyone willing to look closely.

Path

My path through this started with data science, moved through derivatives and credit markets, and landed in asset pricing research.

I worked as a research assistant at Booth with Prof. Dacheng Xiu on empirical asset pricing and ML — building models that separate systematic risk from noise in housing and credit markets. At Harvard Business School, I worked with Profs. Victoria Ivashina and Josh Lerner on illiquidity, return attribution, and causal inference with large-scale firm-level panel data. At IIM Bangalore, I developed pricing models for presale real estate markets with Prof. Venkatesh Panchapagesan. My first real exposure to research was a summer at the Indian Statistical Institute, studying ensemble methods and bias-variance tradeoffs.

On the industry side, I spent time on the derivatives desk at Cboe Global Markets, working on VIX construction and fixed-income index pipelines.

I hold an MS in Financial Mathematics from the University of Chicago (concentration in Financial Data Science) and a BS in Data Science from SP Jain School of Global Management (Mumbai and Sydney).

Beliefs

• Research should reduce ambiguity, not inflate it
• Build tools people can interrogate

Beyond the desk

• Co-founder of The Existology Foundation , a mental health awareness nonprofit — resources that make it a little easier to exist
• Turning playlists into data because taste changes faster than I think
• Cleaning LaTeX more than cleaning rooms
• Long walks where half-baked ideas become usable ones